Anna2 aka A2 the agbot!

Anna2 is a general purpose agricultural robot with an advanced vision system, OpenCV and deep neural network models.

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Anna2 is an agricultural robot built with a Raspberry Pi 3 model B and low cost components to make it an economical labor source. OpenCV and deep neural network models will be used to build the advanced vision system.

Anna2 aka A2 the agbot!

With a growing world population and concerns with the use of pesticides in industrial agriculture endangering bee populations, as well as expensive labor intensive organic farming, A2 will take on several world challenges with a single project.

Problem number 1 world hunger and a growing population:

With a growing world population and only so much available cultivatable land, newer techniques such as high precision agricultural techniques will need to be in use.

Problem number 2 Pesticide and chemical use:

It is believed that pesticide and chemical use could be contributing the bee and pollinator population decreases.

Problem number 3 labor intensive agriculture:

Organic farming and permaculture farming techniques could help the other problems. The problem is these techniques are labor intensive and result in a high price for the products, putting them out of reach for many families, as well as lower production.

A possible solution:

A2 will be a general purpose autonomous as well as tele-operated robot to help try to eliminate the problems. In order to help with high precision agriculture, a robot could give each plant more attention to increase crop yield. With greater attention to each plant, a robot could target specific insects which will help eliminate pests without targeting beneficial insects. It can also kill weeds without killing the crop.

I believe the key to accomplish these goals will be an advanced vision system and the ability to see and identify plants and insects in an agricultural environment. A2 will be built with a Raspberry Pi 3 model B and low cost components to make it an economical labor source. Open CV and deep neural network models will be used to build the advanced vision system.

  • Googles Tensor flow, Deep Learning Neural Network and Raspberry Pi

    Dennis04/08/2017 at 21:29 0 comments

    I wanted to dive into deep learning networks on this project and decided Google’s Tensorflow would be a place to start with a lot of online resources available. I loaded tensorflow, tflearn and the dependencies. Everything seems to be working. Below, the Raspberry Pi is running a linear regression example.

    So far, so good!

  • First step--load the first tool OpenCV:

    Dennis03/31/2017 at 00:35 0 comments

    I loaded OpenCV on the Raspberry Pi. I didn’t think there was a need to go through too many details with a lot of tutorials on loading OpenCV on Raspberrys online. Below is a short program to test OpenCV. I used pygame to display the results on my remote desktop.

    # Set up haarcascades
    HAAR_PATH = "/home/pi/opencv-3.1.0/data/haarcascades"
    # Face
    FACE_HAAR = os.path.join(HAAR_PATH, "haarcascade_frontalface_default.xml")
    face_cascade = cv2.CascadeClassifier(FACE_HAAR)

    #Set up cam and pygame
    cam = cv2.VideoCapture(0)

    #create fullscreen display 640x480
    screen = pygame.display.set_mode((640,480),0)

    ret, frame =
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray_small = cv2.resize(gray, (160,120))
    faces = face_cascade.detectMultiScale(gray_small, 1.3 , 5)
    for (x,y,w,h) in faces:
    cv2.rectangle(frame,(x*4,y*4),((x*4) + (w*4),(y*4) + (h*4)),(255,0,0),2)
    pg_image = pygame.image.frombuffer(frame.tostring(),(640,480) ,"RGB")
    screen.blit(pg_image, (0, 0)) #Load new image on screen
    print len(faces)
    for event in pygame.event.get():
    if event.type == pygame.QUIT:

    I plugged in a USB Camera and ran the program.

    Here’s the results…It works!

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